DESCRIPTION: (Adapted From The Applicant's Abstract.) The ability to process information in sound is demonstrated everyday as we make sense of the complex and continuous pattern of variation in the acoustic signals we encounter. The purpose of the research proposed is to achieve a better understanding of this ability through a formal analysis of the ability to discriminate complex, continuously-varying acoustic patterns other than speech. There are three key elements of this approach. First, all efforts are linked by a single mathematical-methodological framework where the information in a pattern is given precise meaning and listener performance is evaluated relative to a common theoretical standard. Second, the relative extent to which listeners make use of (weight) different sources of information within patterns is determined from trial-by- trial analyses of the data from each experiment. Third, a computational model that has made accurate predictions for the results of many past studies is recruited in the generation of specific hypotheses regarding the outcome of the proposed experiments. These three elements are combined to achieve five specific aims: (1) to provide a formal description of the interaction that causes variation in one acoustic dimension to interfere with processing of information in another, (2) to test current theories of information integration that have been proposed to account for non-additive effects of multiple sources of interference, (3) to identify specific pattern constraints that aid in discrimination, (4) to evaluate the listener's ability to detect complex patterns of statistical variation and covariation within patterns, and (5) to investigate the processing of lawful variation in sound produced by real sound sources. In the last case, the principles of theoretical acoustics are applied to reconstruct the sound pressure waveform at the ear as it is generated by a number of simple resonant objects. The results of the proposed studies will further our understanding of how natural redundancies in patterns aid detection in noisy backgrounds, and how listeners process invariant relations among components that define dynamic properties of patterns like those of speech and other meaningful sounds.